Search experience-based search adaptation in artificial bee colony algorithm

As a relatively new population-based technique for the continuous optimization problems, artificial bee colony algorithm (ABC) has verified its superiority and robustness over traditional evolutionary algorithms. However, regarding the wide variety of ABC extensions, the use of search experience produced during the search progress has been relatively unexplored to improve the performance. This results in an enormous waste of search resource to lower the search efficiency. In this paper, we develop a search experience-based search adaptation (SESA) approach to accelerate the search efficiency of ABC by taking three search resources into account, that is, the successful search directions, the fitness resource and the large number of failed solutions. The experiments over a comprehensive set of benchmark functions demonstrate that SESA can significantly accelerate the search efficiency of ABC.

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